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03_​H2O_​GLM_​Regression_​Model

H2O Generalized Linear Model for regression

This example shows how to build an H2O GLM model for regression, predict new data and score the regression metrics for model evaluation.

1. Prepare:
Load the carspeed data, import the resulting KNIME Table to H2O and partition the data for test and train set 30/70.

2. Learn:
We learn the GBMGLM Model using the "H2O Generalized Linear Model Learner (Regression) using the default algorithm settings.

3. Predict:
Make predictions on test data using the model.

4. Score:
In order to evaluate our model, we asess the accuracy by scoring the predictions made on the test data.

Training a Generalized Linear Model for regression with H2O This tutorial shows how to train an GLM Regression Model using H2O in KNIME. Wewill train a Model and predict the reponse of the carspeed dataset. Finally we scorethe accuracy of our model. 1. Prepare 2. Learn 4. Score 3. Predict Start local H2O NodeLoad carspeed dataPredictScore predictionLearn GLM Regression Model H2O Local Context Table Reader H2O Predictor(Regression) H2O Numeric Scorer Import to H2O andpartition data H2O Generalized LinearModel Learner (Regression) Training a Generalized Linear Model for regression with H2O This tutorial shows how to train an GLM Regression Model using H2O in KNIME. Wewill train a Model and predict the reponse of the carspeed dataset. Finally we scorethe accuracy of our model. 1. Prepare 2. Learn 4. Score 3. Predict Start local H2O NodeLoad carspeed dataPredictScore predictionLearn GLM Regression ModelH2O Local Context Table Reader H2O Predictor(Regression) H2O Numeric Scorer Import to H2O andpartition data H2O Generalized LinearModel Learner (Regression)

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: nico
Created at: 2017-07-10
On NodePit since: 2024-03-08
Last update: 2024-11-18
Created with KNIME version: v4.1.0
Tags: H2OGeneralized Linear Modelmachine learningglm

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